Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep

Motor skills dynamically evolve during practice and after training. Using magnetoencephalography, we investigated the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods after the end of learning (boost window, within 30 min) and at delayed time scales (silent 4 h and next day 24 h windows) with intermediate daytime sleep or wakefulness. Resting-state neural dynamics were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and functional connectivity (FC), respectively, and their relationship to motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training task re-exposure modulated neural network characteristics during the boost but not the silent window. Re-exposure-related induction effects were observed on the next day, to a lesser extent than during the boost window. Daytime naps did not modulate memory consolidation at the behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and memory consolidation and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.


Best motor performance (BMP) calculation
The BMP Index was calculated by averaging for each participant the two best-performed FTT blocks in the Learning session.Table 1 presents the sequence numbers of these two FTT blocks, selected from a total of 20, representing the peak performance instances used in the BMP calculation.

Evolution of motor performance (non-normalised, raw data)
The learning curves observed during the session for both groups aligned with previous findings [1] showcasing a rapid initial improvement that eventually approaches a performance plateau towards the task's conclusion.A repeated measures ANOVA computed on raw GPI scores during the learning session with within-subject factor Blocks (1 to 20) and Group (Nap vs. Wake) factors disclosed a main Blocks effect (F(19,532) = 15.69,p < .001; Figure 1A) with gradually improving performance over practice.The Group effect was significant (F(1,28) = 13,05, p < .001),with on average superior performance levels in the Nap than in the Wake group.However, the Group X Session interaction (F(19,552) = 1.33, p = .16)effect was non-significant, indicating a similar evolution of behavioural performance despite the initial between-group differences.The evolution of offline performance assessed using repeated measures ANOVA on raw GPI scores with Session (BMP x Test 1 x Test 2 x Test 3) and Group (Nap vs. Wake) factors revealed a significant main effect for Session (F(3,84) = 5.45, p = .002),indicating notable changes over time.

Sleep measures
Polysomnographic data (naps and nights) were scored using 20-sec epochs with bandpass 0.3-30 Hz for the scalp electrodes.

Sleep macrostructure and performance
To examine the connections between sleep macrostructure and performance, we performed correlational analyses linking the duration of nap and nighttime sleep (as detailed in Tables 4 and   5) with normalised BMP/LI data.

HMM temporal characteristics
To reveal the impact of motor learning (ML) on HMM temporal parameters, we performed a repeated measures ANOVA with Sessions (T1 vs. T2 vs. T3) and Induction (pre -vs.postbehavioural testing) as within-subject factors and Group (Nap vs. Wake) as between-subjects factor.
The significance level was set at p < .05,Bonferroni corrected by a factor of 21 (i.e., 7 independent HMM states and 3 independent HMM temporal parameters).

Resting-state functional connectivity (rsFC) wideband
The functional connectome of our study included 126 regions of interest (116 nodes correspond to the Automated Anatomical Labelling (AAL) atlas [2] (including the cerebellum).
Additionally, we added ten nodes based on the relevant learning/memory literature (see Table 8).
The MNI coordinates from the literature were derived/adjusted using the SPM Anatomy toolbox, Version 2.2b.To identify the network of regions engaged in motor learning and consolidation, we computed the adjacency matrix (126 by 126) for wideband (4-30 Hz).A statistical within-group comparison of inter-regional connectivity differences was then computed on the adjacency matrix using nonparametric Network Based Statistics (NBS) [3], [4].Our results reveal that re-introducing the task (induction effect) resulted in a vast neural network in the boost window (RS 3 vs.RS 2) comprising 39 edges and 28 nodes p = 0.038.

Resting-state functional connectivity 5-band data
To reveal band-specific rsFC changes induced by motor learning, we computed the adjacency matrix (126 by 126) for five distinct frequency bands: delta (δ 1-4 Hz), theta (θ band: 4-8 Hz), alpha (α band: 8-13 Hz), beta (β band: 13-30 Hz) and gamma (γ band: 30-45 Hz).Next, we performed a statistical within-group comparison of inter-regional connectivity differences as described for the wideband in the previous section.The results of this analysis are illustrated in

Correlational analysis between connectivity and behavioural indices
Correlational analyses between each resting-state RS session and Best Motor Performance Index (BMP) index highlighted a positive correlation between BMP and RS 3 connectivity (boost period, induced).Table 10 shows the list of nodes for the revealed resting-state functional connectivity network.Correlational analysis between rsFC and the Learning index (LI) revealed significant positive correlations only for RS 5 (induced RS, silent period).

State networks connectivity strength
Using HMM power maps, we extracted three sets of nodes (state networks) representing HMM States 3, 7, and 8 (see main text results).To quantify the connectivity strength of these state networks, we calculated the sum of all power envelope correlation values for each node within a given network.Next, we divided this sum by the number of nodes within the network to obtain a normalised measure of connectivity strength.This procedure enabled us to generate a single value for each subject in each resting-state session, which was used in a subsequent correlational analysis with behavioural measures using JASP version 0.16.

Fast and slow neural dynamics relationships
To reveal the relationship between fast and slow neural dynamics, we performed a correlational analysis between HMM temporal parameters and state networks' rsFC using the NBS toolbox.

Figure 2 .
Figure 2. HMM temporal parameters for State 3 (Temporal/Sensorimotor).White violins represent noninduced RS sessions, grey violins -represent induced RS, and the medians are in solid lines and quartilesdotted lines.( # p ≤ .05not corrected for multiple comparisons).

Figure 3 .
Figure 3. HMM temporal parameters for State 5 (Calcarine/Postcentral). White violins represent non-induced RS sessions, grey violins -represent induced RS, and the medians are in solid lines and quartiles -dotted lines; ( # p ≤ .05not corrected for multiple comparisons).

Figure 4 .
Figure 4. HMM temporal parameters for State 6 (Supramarginal).White violins represent non-induced RS sessions, grey violins -represent induced RS, and the medians are in solid lines and quartiles -dotted lines.( # p ≤ .05not corrected for multiple comparisons).

Figure 5 .
Figure 5. Neural network representing increased connectivity in post-learning session (Rest 2 vs. Rest 1) in the theta band.Nodes are scaled according to their weight (the sum of all edges connected to the node).Significant edges are represented as interconnecting lines between 126 connectome seed regions.Abbreviations: PreCG -Precentral gyrus; DN -Dentate nucleus.L -left; R -right.

Figure 6 .
Figure 6.Neural network representing the increased connectivity in the alpha band as a result of induction effect (re-introduction to the task) during the boost period.Nodes are scaled according to their weight (the sum of all edges connected to the node).Significant edges are represented as interconnecting lines between 126 connectome seed regions.Abbreviations: ROL -Rolandic operculum; TMG -Temporal middle gyrus; pMFC -posterior Medial frontal cortex; SMA -Supplementary motor area; DCG -Median cingulate and paracingulate gyri; L -left; R -right.

Figure 7 .
Figure 7. Neural network representing the increased connectivity in the beta band as a result of induction effect (re-introduction to the task) during the boost period.Nodes are scaled according to their weight (the sum of all edges connected to the node).Significant edges are represented as interconnecting lines between 126 connectome seed regions.Abbreviations: SFG -Superior frontal gurus; PreCG -Precentral gyrus; ROL -Rolandic operculum; L -left; R -right.

Figure 8 .
Figure 8. Neural network representing the increased connectivity in the delta band as a result of induction effect (re-introduction to the task) during the boost period.Nodes are scaled according to their weight (the sum of all edges connected to the node).Significant edges are represented as interconnecting lines between 126 connectome seed regions.Abbreviations: IFGoperc -Inferior frontal gyrus, opercular part; MOG -Middle occipital gyrus; CAU -Caudate nucleus; CUN -Cuneus; L -left; R -right.

Figure 9 .
Figure 9. Neural network representing the increased connectivity in gamma band as a result of induction effect (re-introduction to the task) during the boost period.Nodes are scaled according to their weight (the sum of all edges connected to the node).Significant edges are represented as interconnecting lines between 126 connectome seed regions.Abbreviations: ORBinf -Inferior frontal gyrus, orbital part; ACG -Anterior cingulate and paracingulate gyri; SFGmed -Superior frontal gyrus, medial; CRB 6 -Cerebellum lobe 6; Lleft; R -right.

Figure 10 .
Figure 10.Neural network representing the increased connectivity in alpha band as a result of induction effect (re-introduction to the task) during the next day period.Nodes are scaled according to their weight (the sum of all edges connected to the node).Significant edges are represented as interconnecting lines between 126 connectome seed regions.Abbreviations: PreCG -Precental gyrus; PCUN -Precuneus; ORBsup -Superior frontal gyrus, orbital part; PoCG -Postcentral gyrus; ORBsupmed -Superior frontal gyrus, medial orbital; L -left; R -right.

Figure 11 .
Figure 11.Neural network representing the increased connectivity in gamma band as a result of induction effect (re-introduction to the task) during the next day period.Nodes are scaled according to their weight (the sum of all edges connected to the node).Significant edges are represented as interconnecting lines between 126 connectome seed regions.Abbreviations: PCL -Paracentral lobule; DCG -Median cingulate and paracingulate gyri; CRB Crus 1 -Cerebellum Crus 1; SMA -Supplementary motor area; L -left; R -right.

Table 1 .
FTT blocks used for BMP estimation (average of the two FTT blocks with the best performance) in the Learning session.

Table 2 .
Sleep scoring in the 90-min nap conditions.Mean values and standard errors indicate the duration (min) and percentage (%) of time spent in each stage; the range is indicated in brackets.The range of data is in brackets.Abbreviations: NREM (non-rapid eye movement sleep), SWS (slow wave sleep), REM (rapideye movement sleep), TST (total sleep time, considers all sleep stages except wakefulness).

Table 3 .
Nocturnal sleep scorings in the Wake and Nap groups.The mean values and standard errors indicate the duration (min) and percentage (%) of time spent in each stage; the range is indicated in brackets.

Table 4 .
Correlations between duration of nap sleep stages and behavioural (normalized, Z-score) indices.

Table 5 .
Correlations between duration of night sleep stages and behavioural (normalized, Z-score) indices.

Table 6
represents the results of repeated measures ANOVA for States 3, 4, 5 and 6.Figures2-4illustrate the results for these HMM states.

Table 7
represents the results of post-hoc analyses conducted on Induction effects within each session using a paired-sampled Wilcoxon signed-rank test for all HMM states.

Table 9
represents the nodes list of this emerged network.

Table 8 .
Full connectome node list (116 AAL nodes (not highlighted); 10 additional nodes inspired by literature (in grey).The MNI coordinates of additional nodes derived/were checked with the Anatomy toolbox (SPM).

Table 9 .
The node list of the wideband resting-state functional connectivity (rsFC) analysis for the induction effect (Rest 3 vs.Rest 2) in the boost time window.

Table 10 .
Nodes list of the neural network which positively correlated with Best Motor Performance (BMP).

Table 11 . Nodes list of the neural network which positively correlated with the Learning index
Table 11 lists the nodes from the emerged rsFC network.

Table 11 .
Nodes list of the neural network which positively correlated with the Learning index (LI).

Table 12 . The node list of the HMM state 7 (Frontal/Cuneus) network
2, JASP Team (2022).The results disclosed positive correlations between BMP and State 7 (Frontal/Cuneus) network.Table 12 lists the nodes comprising the network.

Table 12 .
The node list of the HMM state 7 (Frontal/Cuneus) network.

Table 13 . Correlations between HMM temporal parameters (TP) and HMM State 8 (Cuneus/Sensorimotor) network
Table 13 illustrates the correlational outcomes for State 8.